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                    Introduction
            
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                    Android
            
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                    Machine Learning
            
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                    Matlab Remote IPC自动化数据处理
            
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                    Papers
            
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                    Classification
            
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                    Re-identification
            
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                    CVPR2018:TFusion完全解读
            
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                    ECCV2018:TAUDL
            
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                    CVPR2018:Graph+reid
            
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                    Person Re-identification
            
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                    CVPR2016 Re-id
            
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                    Camera topology and Person Re-id
            
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                    Deep transfer learning Person Re-id
            
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                    Evaluate
            
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                    Object Detection
            
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                    读论文系列·干货满满的RCNN
            
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                    读论文系列·SPP-net
            
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                    读论文系列·Fast RCNN
            
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                    读论文系列·Faster RCNN
            
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                    读论文系列·YOLO
            
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                    读论文系列·SSD
            
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                    读论文系列·YOLOv2 & YOLOv3
            
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                    读论文系列·detection其他文章推荐
            
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        <li class="chapter " data-level="1.4.4.4" data-path="../mono_depth/">
            
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                    Depth
            
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        <li class="chapter " data-level="1.4.4.5" data-path="../3d_vision/">
            
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                    3D vision
            
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                    数据集相关
            
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                    光流相关
            
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                    Hashing
            
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                    CVPR2018: SSAH
            
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        <li class="chapter " data-level="1.4.4.7" data-path="../overview/">
            
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                    大杂烩
            
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                    CNCC2017 琐记
            
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                    ECCV 2016 Hydra CCNN
            
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                    CNCC2017深度学习与跨媒体智能
            
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                    MLA2016笔记
            
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        <li class="chapter " data-level="1.4.5" data-path="../../zzh_ml_notes/">
            
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                    《机器学习》（周志华）读书笔记
            
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                    西瓜书概念整理
            
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                    绪论
            
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                    模型评估与选择
            
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                    线性模型
            
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                    决策树
            
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                    神经网络
            
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                    支持向量机
            
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        <li class="chapter " data-level="1.4.5.1.7" data-path="../../zzh_ml_notes/melon/ch07.html">
            
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                    贝叶斯分类器
            
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        <li class="chapter " data-level="1.4.5.1.8" data-path="../../zzh_ml_notes/melon/ch08.html">
            
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                    集成学习
            
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        <li class="chapter " data-level="1.4.5.1.9" data-path="../../zzh_ml_notes/melon/ch09.html">
            
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                    聚类
            
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        <li class="chapter " data-level="1.4.5.1.10" data-path="../../zzh_ml_notes/melon/ch10.html">
            
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                    降维与度量学习
            
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        <li class="chapter " data-level="1.4.5.1.11" data-path="../../zzh_ml_notes/melon/ch11.html">
            
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                    特征选择与稀疏学习
            
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        <li class="chapter " data-level="1.4.5.1.12" data-path="../../zzh_ml_notes/melon/ch12.html">
            
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                    计算学习理论
            
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        <li class="chapter " data-level="1.4.5.1.13" data-path="../../zzh_ml_notes/melon/ch13.html">
            
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                    半监督学习
            
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        <li class="chapter " data-level="1.4.5.1.14" data-path="../../zzh_ml_notes/melon/ch14.html">
            
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                    概率图模型
            
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                                <h1 id="&#x5206;&#x7C7B;&#x7F51;&#x7EDC;&#x6700;&#x65B0;&#x8FDB;&#x5C55;">&#x5206;&#x7C7B;&#x7F51;&#x7EDC;&#x6700;&#x65B0;&#x8FDB;&#x5C55;</h1>
<h2 id="self-training-with-noisy-student-improves-imagenet-classification">Self-training with Noisy Student improves ImageNet classification</h2>
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<p>CVPR2020</p>
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<h3 id="method">Method</h3>
<ul>
<li>&#x5728;&#x6709;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x8BAD;&#x7EC3;Teacher,&#x5728;&#x65E0;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x9884;&#x6D4B;&#x4F2A;&#x6807;&#x7B7E;</li>
<li>&#x7528;&#x6709;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x548C;&#x4F2A;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4ECE;&#x5934;&#x8BAD;&#x7EC3;&#x4E00;&#x4E2A;Student&#x6A21;&#x578B;&#xFF0C;Student&#x6A21;&#x578B;</li>
<li>&#x628A;Student&#x5F53;&#x6210;Teacher&#xFF0C;&#x8FED;&#x4EE3;&#xFF0C;&#x6548;&#x679C;&#x6700;&#x597D;&#x7684;&#x662F;&#x8FED;&#x4EE3;&#x4E09;&#x6B21;</li>
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<p>&#x7279;&#x522B;&#x7684;&#x5730;&#x65B9;&#x5728;&#x4E8E;&#xFF1A;</p>
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<li>model noise: Student&#x6BD4;Teacher&#x5927;&#xFF0C;&#x591A;&#x4E86;Dropout&#x548C;stochastic depth(&#x8BAD;&#x7EC3;&#x65F6;&#xFF0C;&#x91CD;&#x590D;&#x7684;block&#x4E2D;&#xFF0C;&#x67D0;&#x4E9B;&#x4F1A;&#x88AB;&#x8BBE;&#x7F6E;&#x6210;identity)</li>
<li>data noise:&#x8BAD;&#x7EC3;&#x6570;&#x636E;&#x52A0;Data augumentation&#xFF08;&#xFF09;</li>
<li>&#x4E00;&#x4E9B;trick&#xFF1A;<ul>
<li>Teacher&#x7F51;&#x7EDC;&#x7F6E;&#x4FE1;&#x5EA6;&#x4F4E;&#x7684;&#xFF0C;&#x4E0D;&#x4F1A;&#x7528;&#x4E8E;&#x8BAD;&#x7EC3;&#xFF1B;</li>
<li>ImageNet&#x6BCF;&#x4E2A;&#x7C7B;&#x7684;&#x56FE;&#x50CF;&#x6570;&#x91CF;&#x5DEE;&#x4E0D;&#x591A;&#xFF0C;&#x56E0;&#x6B64;&#x5E26;&#x4F2A;&#x6807;&#x7B7E;&#x7684;unlabel data&#x4E5F;&#x5728;&#x7C7B;&#x522B;&#x4E0A;&#x505A;&#x4E86;&#x5E73;&#x8861;&#xFF0C;&#x6570;&#x91CF;&#x591A;&#x7684;&#x7C7B;&#x53EA;&#x4FDD;&#x5B58;&#x4E00;&#x90E8;&#x5206;&#xFF0C;&#x6570;&#x91CF;&#x5C11;&#x7684;&#x7C7B;&#x591A;&#x590D;&#x5236;&#x51E0;&#x4EFD;</li>
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<h3 id="&#x6548;&#x679C;">&#x6548;&#x679C;</h3>
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<h2 id="meta-pseudo-labels">Meta Pseudo Labels</h2>
<h3 id="motivation">Motivation</h3>
<p>&#x4F7F;&#x7528;&#x4F2A;&#x6807;&#x7B7E;&#x65E0;&#x76D1;&#x7763;&#x5B66;&#x4E60;&#x7684;&#x65B9;&#x6CD5;&#xFF0C;&#x56E0;&#x4E3A;&#x4F2A;&#x6807;&#x7B7E;&#x53EF;&#x80FD;&#x662F;&#x9519;&#x7684;&#xFF0C;&#x6240;&#x4EE5;Student&#x4E0D;&#x80FD;&#x8D85;&#x8D8A;Teacher&#xFF0C;&#x5982;&#x679C;&#x80FD;&#x8BA9;Teacher&#x5728;&#x8FC7;&#x7A0B;&#x4E2D;&#x4E5F;&#x5F97;&#x5230;&#x8BAD;&#x7EC3;&#xFF0C;&#x8F93;&#x51FA;&#x66F4;&#x597D;&#x7684;&#x6807;&#x7B7E;&#xFF0C;Student&#x5C31;&#x6709;&#x5E0C;&#x671B;&#x8F93;&#x51FA;&#x66F4;&#x597D;&#x7684;&#x7ED3;&#x679C;&#x3002;</p>
<h3 id="method">Method</h3>
<ul>
<li>&#x5728;&#x6709;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x5148;&#x8BAD;&#x7EC3;&#x4E00;&#x4E2A;Teacher&#xFF0C;&#x5728;&#x65E0;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x9884;&#x6D4B;&#x4F2A;&#x6807;&#x7B7E;</li>
<li>&#x7528;&#x4F2A;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x8BAD;&#x7EC3;Student</li>
<li>Student&#x5728;&#x6709;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x6D4B;&#x8BD5;&#xFF0C;&#x5F97;&#x5230;Loss</li>
<li>Teacher&#x6839;&#x636E;Student&#x5728;&#x6709;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x7684;Loss&#xFF0C;&#x5BF9;&#x81EA;&#x8EAB;&#x6743;&#x91CD;&#x8FDB;&#x884C;&#x4FEE;&#x6B63;&#xFF08;&#x5173;&#x952E;&#x5C31;&#x5728;&#x4E8E;&#x5982;&#x4F55;&#x4FEE;&#x6B63;&#xFF09;</li>
</ul>
<h3 id="detail">Detail</h3>
<p>&#x5047;&#x5982;&#x7528;&#x4F2A;&#x6807;&#x7B7E;&#x65B9;&#x6848;&#x8BAD;&#x7EC3;Student&#xFF0C;&#x90A3;&#x4E48;&#x8BAD;&#x7EC3;&#x76EE;&#x6807;&#x53EF;&#x4EE5;&#x8FD9;&#x6837;&#x8868;&#x793A;&#xFF1A;
<script type="math/tex; ">\theta_{S}^PL = argmin_{\theta_S} L_u(\theta_T, \theta_S)</script></p>
<p>&#x5176;&#x4E2D;&#xFF0C;<script type="math/tex; ">L_u(\theta_T, \theta_S) = E_{x_u}[CE(T(x_u; \theta_T), S(x_u; \theta_S))]</script></p>
<p>&#x5982;&#x679C;&#x6839;&#x636E;Student&#x5728;&#x6709;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x96C6;&#x4E0A;&#x7684;&#x8868;&#x73B0;&#xFF0C;&#x4F18;&#x5316;Teacher&#xFF0C;&#x90A3;&#x8FD9;&#x4E2A;&#x76EE;&#x6807;&#x53EF;&#x4EE5;&#x8868;&#x793A;&#x4E3A;&#xFF1A;<script type="math/tex; "> min_{\theta_T} L_l (\theta_{S}^{PL}(\theta_T))</script></p>
<p>&#x5176;&#x4E2D;&#xFF0C;<script type="math/tex; ">\theta_{S}^{PL}(\theta_T)</script>&#x5C31;&#x662F;&#x4F2A;&#x6807;&#x7B7E;&#x65B9;&#x6848;&#x4E0B;&#xFF0C;&#x5728;Teacher&#x7F51;&#x7EDC;&#x7684;&#x53C2;&#x6570;<script type="math/tex; ">\theta_T</script>&#x4E0B;&#xFF0C;&#x4F18;&#x5316;&#x51FA;&#x7684;&#x6700;&#x4F18;Student&#x53C2;&#x6570;&#xFF0C;&#x5373;<script type="math/tex; ">argmin_{\theta_S} L_u(\theta_T, \theta_S)</script></p>
<p>&#x6240;&#x4EE5;&#x95EE;&#x9898;&#x5C31;&#x5728;&#x4E8E;&#x5982;&#x4F55;&#x89E3;&#x8FD9;&#x4E2A;&#x4F18;&#x5316;&#x95EE;&#x9898;&#xFF1A;<script type="math/tex; "> min_{\theta_T} L_l (\theta_{S}^{PL}(\theta_T))</script>&#xFF0C;</p>
<p>&#x9996;&#x5148;<script type="math/tex; ">\theta_{S}^{PL}(\theta_T)</script>&#x53EF;&#x4EE5;&#x901A;&#x8FC7;&#x68AF;&#x5EA6;&#x4E0B;&#x964D;&#x6CD5;&#x8FED;&#x4EE3;&#x5F97;&#x5230;&#xFF0C;&#x4F46;&#x5B83;&#x662F;&#x4E00;&#x4E2A;&#x591A;&#x6B65;&#x7684;&#x4F18;&#x5316;&#x8FC7;&#x7A0B;&#x624D;&#x80FD;&#x5F97;&#x5230;&#x7684;&#x7ED3;&#x679C;&#xFF0C;&#x6211;&#x4EEC;&#x7528;&#x5355;&#x6B65;&#x4F18;&#x5316;&#x7684;&#x7ED3;&#x679C;&#x6765;&#x8FD1;&#x4F3C;&#xFF1A;<script type="math/tex; ">\theta_{S}^{PL}(\theta_T)=\theta_S - \eta_S * \triangledown_{\theta_S}L_u(\theta_T, \theta_S)</script>&#xFF0C;&#x90A3;&#x4E48;&#x4F18;&#x5316;&#x76EE;&#x6807;&#x5C31;&#x53D8;&#x6210;&#x4E86;&#xFF1A;<script type="math/tex; "> min_{\theta_T} L_l (\theta_S - \eta_S * \triangledown_{\theta_S}L_u(\theta_T, \theta_S))</script>;</p>
<p>&#x90A3;&#x4E48;&#x6BCF;&#x5728;&#x4F2A;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x66F4;&#x65B0;&#x4E00;&#x6B21;Student&#xFF0C;&#x5C31;&#x53EF;&#x4EE5;&#x5728;&#x6709;&#x6807;&#x7B7E;&#x6570;&#x636E;&#x4E0A;&#x7B97;&#x51FA;Teacher&#x7684;&#x6743;&#x91CD;&#x66F4;&#x65B0;&#x91CF;&#xFF0C;&#x5BF9;Teacher&#x8FDB;&#x884C;&#x66F4;&#x65B0;&#xFF1A;<script type="math/tex; ">\theta_T=\theta_T-\eta_T\triangledown_{\theta_T} L_l (\theta_S - \eta_S * \triangledown_{\theta_S}L_u(\theta_T, \theta_S))</script>&#xFF0C;&#x5176;&#x4E2D;&#x4E24;&#x4E2A;&#x68AF;&#x5EA6;&#x90FD;&#x662F;&#x5BF9;CE&#x6C42;&#x5BFC;&#xFF0C;&#x56E0;&#x6B64;&#x8FD9;&#x4E2A;&#x66F4;&#x65B0;&#x91CF;&#x53EF;&#x4EE5;&#x5F88;&#x5BB9;&#x6613;&#x5730;&#x6C42;&#x51FA;&#x6765;&#xFF1B;</p>
<h2 id="&#x8F85;&#x52A9;loss">&#x8F85;&#x52A9;Loss</h2>
<p>&#x5355;&#x72EC;&#x7528;&#x4E0A;&#x9762;&#x7684;&#x65B9;&#x6CD5;&#x5DF2;&#x7ECF;&#x53EF;&#x4EE5;&#x5F97;&#x5230;&#x4E0D;&#x9519;&#x7684;&#x7ED3;&#x679C;&#x4E86;&#xFF0C;&#x4F46;&#x5982;&#x679C;&#x5728;&#x8BAD;&#x7EC3;&#x7684;&#x65F6;&#x5019;&#xFF0C;&#x914D;&#x5408;&#x5176;&#x4ED6;Loss&#x8BAD;&#x7EC3;&#xFF0C;&#x6548;&#x679C;&#x66F4;&#x4F73;&#xFF1B;&#x6587;&#x4E2D;&#x63D0;&#x53CA;&#x4E86;unsupervised domain adaption&#x7684;&#x4E00;&#x4E9B;&#x65B9;&#x6CD5;&#xFF0C;&#x53EF;&#x4EE5;&#x5728;&#x8BBA;&#x6587;&#x9644;&#x5F55;&#x67E5;&#x8BE2;&#x5230;&#x3002;</p>
<h3 id="&#x6548;&#x679C;">&#x6548;&#x679C;</h3>
<p>&#x5728;ImageNet&#x4E0A;&#x9996;&#x6B21;&#x5E72;&#x5230;&#x4E86;90.2%&#x7684;top1 ACC</p>
<h2 id="efficientnet-rethinking-model-scaling-for-convolutional-neural-networks">EfficientNet: Rethinking model scaling for convolutional neural networks</h2>
<h3 id="motivation">Motivation</h3>
<p>&#x8FD9;&#x7BC7;&#x8BBA;&#x6587;&#x6211;&#x66F4;&#x613F;&#x610F;&#x79F0;&#x5176;&#x4E3A;&#x5B9E;&#x9A8C;&#x62A5;&#x544A;&#xFF0C;&#x7814;&#x7A76;&#x4E86;&#x5982;&#x4F55;&#x6269;&#x5C55;&#x7F51;&#x7EDC;&#x7684;input resolution, width(channel&#x6570;)&#xFF0C; depth(layer&#x6570;)&#xFF0C;&#x624D;&#x80FD;&#x5F97;&#x5230;&#x6027;&#x4EF7;&#x6BD4;&#x6700;&#x9AD8;&#x7684;&#x7F51;&#x7EDC;</p>
<h3 id="observation">Observation</h3>
<p>&#x8FD9;&#x7BC7;&#x6587;&#x7AE0;&#x57FA;&#x4E8E;&#x4E24;&#x4E2A;observation&#x6765;&#x8C03;&#x6574;&#x7F51;&#x7EDC;&#x89C4;&#x6A21;&#xFF1A;</p>
<ul>
<li>&#x589E;&#x52A0;&#x8F93;&#x5165;/&#x5BBD;&#x5EA6;/&#x6DF1;&#x5EA6;&#xFF0C;&#x90FD;&#x80FD;&#x63D0;&#x9AD8;&#x6A21;&#x578B;&#x6548;&#x679C;&#xFF0C;&#x4F46;&#x5B58;&#x5728;&#x8FB9;&#x9645;&#x6548;&#x5E94;&#xFF0C;&#x52A0;&#x7684;&#x592A;&#x591A;&#x63D0;&#x5347;&#x4E0D;&#x660E;&#x663E;&#xFF1B;</li>
<li>&#x56E0;&#x6B64;&#xFF0C;&#x4E0E;&#x5176;&#x628A;&#x591A;&#x51FA;&#x6765;&#x7684;&#x7B97;&#x529B;&#x5806;&#x5728;&#x5355;&#x4E2A;&#x7EF4;&#x5EA6;&#x4E0A;&#xFF0C;&#x4E0D;&#x5982;&#x540C;&#x65F6;&#x6269;&#x5F20;&#x4E09;&#x4E2A;&#x7EF4;&#x5EA6;&#xFF1B;</li>
</ul>
<h3 id="method">Method</h3>
<p>&#x5177;&#x4F53;&#x7684;&#x6269;&#x5F20;&#x7B56;&#x7565;&#x5982;&#x4E0B;&#xFF1A;</p>
<ul>
<li>&#x5047;&#x8BBE;depth&#x53D8;&#x6210;<script type="math/tex; ">\alpha</script>&#x500D;&#xFF0C;&#x90A3;&#x4E48;FLOPS&#x4F1A;&#x53D8;&#x4E3A;<script type="math/tex; ">\alpha</script>&#x500D;&#xFF0C;&#x5047;&#x8BBE;width(&#x5377;&#x79EF;&#x7684;&#x8F93;&#x5165;&#x548C;&#x8F93;&#x51FA;&#x901A;&#x9053;&#x6570;)&#x53D8;&#x4E3A;<script type="math/tex; ">\beta</script>&#x500D;&#xFF0C;&#x90A3;&#x4E48;FLOPS&#x4F1A;&#x53D8;&#x4E3A;<script type="math/tex; ">\beta^2</script>&#x500D;&#xFF0C;&#x5047;&#x8BBE;&#x8F93;&#x5165;&#x5BBD;&#x5EA6;&#x53D8;&#x4E3A;<script type="math/tex; ">\gamma</script>&#xFF0C;&#x90A3;&#x4E48;FLOPS&#x53D8;&#x4E3A;<script type="math/tex; ">\gamma^2</script>&#xFF1B;</li>
<li>&#x6211;&#x4EEC;&#x5E0C;&#x671B;&#x8C03;&#x6574;&#x53C2;&#x6570;&#x540E;&#xFF0C;FLOPS&#x53D8;&#x4E3A;<script type="math/tex; ">2^{\phi}</script>&#xFF0C;&#x90A3;&#x4E48;&#x5C31;&#x8981;&#x6C42;<script type="math/tex; ">\alpha * \beta^2 * \gamma^2=2</script>&#xFF0C;&#x56E0;&#x6B64;&#x5177;&#x4F53;&#x7684;&#x8C03;&#x6574;&#x7B56;&#x7565;&#x5C31;&#x53D8;&#x6210;&#x4E86;&#xFF1A;</li>
<li>depth: <script type="math/tex; ">d=\alpha^\phi</script></li>
<li>width: <script type="math/tex; ">w=\beta^\phi</script></li>
<li>resolution: <script type="math/tex; ">r = \gamma^\phi</script></li>
<li>st: <script type="math/tex; ">\alpha * \beta^2 * \gamma^2=2</script>, <script type="math/tex; ">\alpha>1, \beta>1, \gamma>1</script></li>
</ul>
<h3 id="network">Network</h3>
<p>&#x5728;&#x5404;&#x79CD;&#x57FA;&#x7840;&#x7F51;&#x7EDC;&#x4E0A;&#x8BD5;&#x4E86;&#x8FD9;&#x4E2A;&#x7B56;&#x7565;&#xFF0C;&#x53D1;&#x73B0;Mnas&#x4E0A;&#x6539;&#x51FA;&#x6765;&#x7684;&#x6700;&#x597D;&#xFF0C;&#x7F51;&#x7EDC;&#x7ED3;&#x6784;&#x5927;&#x6982;&#x957F;&#x8FD9;&#x6837;&#xFF0C;&#x4F46;&#x662F;size&#x4F1A;&#x6709;&#x6240;&#x4E0D;&#x540C;&#xFF0C;&#x6BD4;&#x5982;EfficientNet-B0&#x5C31;&#x662F;</p>
<p><img src="mnasnet.png" alt=""></p>
<h3 id="result">Result</h3>
<p><img src="efficentnet.png" alt=""></p>
<h2 id="mlp-mixer-an-all-mlp-architecture-for-vision">MLP-Mixer: An all-MLP Architecture for Vision</h2>
<p>&#x4F7F;&#x7528;MLP&#x4EE3;&#x66FF;&#x5377;&#x79EF;&#xFF0C;&#x4FDD;&#x7559;skip connection&#x548C;normalization&#xFF0C;&#x5728;ImageNet&#x4E0A;&#x6700;&#x9AD8;&#x8FBE;&#x5230;87.94%</p>
<h3 id="method">Method</h3>
<ul>
<li>&#x5C06;&#x56FE;&#x50CF;&#x5206;&#x6210;S&#x4E2A;PxP&#x5927;&#x5C0F;&#x7684;patch&#xFF0C;&#x6BCF;&#x4E2A;patch&#xFF08;n c p p&#xFF09; reshape&#x6210;(n c p*p)&#x7684;tensor</li>
<li>&#x7528;&#x4E00;&#x4E2A;&#x77E9;&#x9635;&#x4E58;&#x6CD5;&#x5148;&#x5C06;&#x6BCF;&#x4E2A;patch&#x5904;&#x7406;&#x4E00;&#x4E0B;reproject&#x5230;n <em> C,&#x6240;&#x6709; patch&#x5408;&#x8D77;&#x6765;&#x5C31;&#x662F;n </em> S * C&#x7684;tensor X</li>
<li>&#x63A5;&#x4E0B;&#x6765;&#x5F00;&#x59CB;mlp&#xFF1A;<ul>
<li><script type="math/tex; ">U=X+W_2\sigma(W_1 Layer Norm(X))</script></li>
<li>&#x5BF9;X&#x505A;layer norm</li>
<li>&#x5148;&#x5BF9;&#x6BCF;&#x4E2A;patch&#x505A;&#x4E58;&#x6CD5;&#xFF0C;<script type="math/tex; ">WX</script>&#xFF0C;&#x53D8;&#x6210;<script type="math/tex; ">n*S1*C</script></li>
<li>&#x505A;&#x4E00;&#x4E2A;&#x975E;&#x7EBF;&#x6027;&#x53D8;&#x6362;<script type="math/tex; ">\sigma</script>&#xFF0C;&#x8FD9;&#x91CC;&#x7528;&#x7684;&#x662F;GELU</li>
<li>&#x518D;&#x505A;&#x4E00;&#x4E2A;&#x4E58;&#x6CD5;&#xFF0C;&#x4ECE;<script type="math/tex; ">n*S1*C</script>&#x53D8;&#x6210;<script type="math/tex; ">n*S*C</script></li>
<li>&#x518D;&#x505A;&#x4E00;&#x4E2A;&#x6B8B;&#x5DEE;&#x52A0;&#x4E0A;X</li>
<li>&#x8FD9;&#x91CC;&#x4E0D;&#x540C;channel&#x4E4B;&#x95F4;&#x7684;&#x6743;&#x91CD;&#x662F;&#x5171;&#x4EAB;&#x7684;&#xFF0C;&#x7528;&#x6765;&#x51CF;&#x5C11;&#x53C2;&#x6570;&#x6570;&#x91CF;</li>
<li>&#x7531;&#x4E8E;&#x4E2D;&#x95F4;token&#x6570;&#x91CF;&#x53D8;&#x4E86;&#xFF0C;patch&#x6570;&#x76EE;&#x6CA1;&#x53D8;&#xFF0C;&#x8D77;&#x4E00;&#x4E2A;&#x82B1;&#x91CC;&#x80E1;&#x54E8;&#x7684;&#x540D;&#x5B57;&#xFF0C;&#x628A;patch&#x53EB;&#x505A;token&#xFF0C;&#x4E58;&#x6CD5;&#x53EB;&#x505A;token-mixing&#xFF0C;&#x4E5F;&#x5C31;&#x662F;&#x5C06;&#x540C;&#x4E2A;&#x901A;&#x9053;&#x4E0D;&#x540C;token&#x5148;&#x6DF7;&#x5408;&#x4E00;&#x4E0B;</li>
<li>&#x518D;&#x505A;&#x4E00;&#x4E2A;MLP&#xFF1A;<script type="math/tex; ">Y=U+W_4\sigma(W_3 Layer Norm(U))</script></li>
<li>&#x4E5F;&#x662F;&#x4E00;&#x6837;&#x7684;&#x64CD;&#x4F5C;&#xFF0C;&#x4E0D;&#x540C;&#x4E4B;&#x5904;&#x5728;&#x4E8E;&#x4E2D;&#x95F4;&#x662F;channel&#x6570;&#x91CF;&#x53D8;&#x4E86;&#xFF0C;&#x540E;&#x9762;&#x53C8;&#x53D8;&#x56DE;&#x6765;&#xFF0C;&#x6240;&#x4EE5;&#x53EB;channel-mixing</li>
</ul>
</li>
<li>&#x6BCF;&#x4E2A;mlp-mixer block&#x90FD;&#x505A;&#x56DB;&#x6B21;&#x4E58;&#x6CD5;&#xFF0C;&#x628A;<script type="math/tex; ">n*S*C</script>&#x7684;tensor&#x53D8;&#x6210;<script type="math/tex; ">n*S*C</script></li>
<li>&#x7F51;&#x7EDC;&#x6700;&#x540E;&#x63A5;&#x4E00;&#x4E2A;Global Average Pooling&#x505A;&#x5206;&#x7C7B;</li>
<li>&#x8BAD;&#x7EC3;&#x7684;&#x65F6;&#x5019;&#x4E5F;&#x662F;&#x4E00;&#x6837;&#x9884;&#x8BAD;&#x7EC3;+finetune</li>
</ul>
<h3 id="mlp-mixer&#x7684;&#x5377;&#x79EF;&#x672C;&#x8D28;">MLP-Mixer&#x7684;&#x5377;&#x79EF;&#x672C;&#x8D28;</h3>
<ul>
<li>&#x4E00;&#x5F00;&#x59CB;&#x5206;&#x4E0D;&#x540C;patch&#xFF0C;&#x5176;&#x5B9E;&#x5C31;&#x662F;&#x4E00;&#x4E2A;<script type="math/tex; ">P*P</script>&#xFF0C;stride&#x4E3A;<script type="math/tex; ">P*P</script>&#x7684;&#x5377;&#x79EF;</li>
<li>&#x4E0D;&#x540C;&#x4F4D;&#x7F6E;&#x76F8;&#x540C;&#x901A;&#x9053;&#x7684;&#x6DF7;&#x5408;&#xFF0C;&#x53EF;&#x4EE5;&#x7528;&#x6DF1;&#x5EA6;&#x53EF;&#x5206;&#x79BB;&#x5377;&#x79EF;&#x4EE3;&#x66FF;
&#xB7;- &#x540C;&#x4E2A;&#x4F4D;&#x7F6E;&#x4E0D;&#x540C;&#x901A;&#x9053;&#x7684;&#x6DF7;&#x5408;&#xFF0C;&#x53EF;&#x4EE5;&#x7528;&#x4E00;&#x4E2A;1x1&#x7684;&#x5377;&#x79EF;&#x4EE3;&#x66FF;</li>
</ul>

                                
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